2P081 Heme structure and drug binding affinities of human CYP2C9 : Effect of SNPs

The distribution of drugs and dietary phenolic compounds in the systemic circulation de-pends on, among other factors, unspecific/specific reversible binding to plasma proteins such as human serum albumin (HSA). Phenolic substances, present in plant-derived feeds, foods, beverages, herbal medicines, and dietary supplements, are of great interest due to their biological activity. Recently, considerable research has been directed at the formation of phenol–HSA complexes, focusing above all on structure–affinity relationships. The nucleophilicity and planarity of molecules can be altered by the number and position of hydroxyl groups on the aromatic ring and by hydrogenation. Binding affinities towards HSA may also differ between phenolic compounds in their native form and conjugates derived from phase II reactions. On the other hand, food–drug interactions may increase the concentration of free drugs in the blood, affecting their transport and/or disposition and in some cases provoking adverse or toxic effects. This is caused mainly by a decrease in drug binding affinities for HSA in the presence of flavonoids. Accordingly, to avoid the side effects arising from changes in plasma protein binding, the intake of flavonoid-rich food and beverages should be taken into consideration when treating certain pathologies.

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Spectroscopic Studies of DNA-Drug Binding Affinities: Sequence Context and Methylation Effects

Biophysical Journal ◽

10.1016/j.bpj.2009.12.3602 ◽

2010 ◽

Vol 98 (3) ◽

pp. 657a

Author(s):

Mary E. Hatcher ◽

Dianna Buckett ◽

Stephanie McCarty

Keyword(s):

Spectroscopic Studies ◽

Drug Binding ◽

Binding Affinities ◽

Sequence Context

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Computational Pharmacogenetics of P-Glycoprotein Mediated Antiepileptic Drug Resistance

The Open Bioinformatics Journal ◽

10.2174/1875036201811010197 ◽

2018 ◽

Vol 11 (1) ◽

pp. 197-207 ◽

Cited By ~ 2

Author(s):

Sindhu Varghese ◽

Ashok Palaniappan

Keyword(s):

Drug Resistance ◽

Treatment Failure ◽

Conformational Changes ◽

Binding Sites ◽

Docking Studies ◽

Drug Binding ◽

Experimental Investigations ◽

Binding Affinities ◽

Drug Binding Site ◽

P Glycoprotein

Background:The treatment of epilepsy using antiepileptogenic drugs is complicated by drug resistance, resulting in treatment failure in more than one-third of cases. Human P-glycoprotein (hPGP;MDR1) is a known epileptogenic mediator.Methods:Given that experimental investigations have suggested a role for pharmacogenetics in this treatment failure, it would be of interest to study hPGP polymorphisms that might contribute to the emergence of drug resistance. Changes in protein functional activity could result from mutations as well as altered abundance. Bioinformatics approaches were used to assess and rank the functional impact of 20 missenseMDR1polymorphisms and the top five were selected. The structures of the wildtype and variant hPGP were modelled based on the mouse PGP structure. Docking studies of the wildtype and variant hPGP with four standard anti-epileptic drugs were carried out.Results:Our results revealed that the drug binding site with respect to the wildtype protein was uniform. However, the variant hPGP proteins displayed a repertoire of binding sites with stronger binding affinities towards the drug.Conclusion:Our studies indicated that specific polymorphisms inMDR1could drive conformational changes of PGP structure, facilitating altered contacts with drug-substrates and thus modifying their bioavailability. This suggests thatMDR1polymorphisms could actively contribute to the emergence of pharmaco-resistance in antiepileptic therapy.

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Enhanced Drug Delivery to Solid Tumors via Drug-Loaded Nanocarriers: An Image-Based Computational Framework

Frontiers in Oncology ◽

10.3389/fonc.2021.655781 ◽

2021 ◽

Vol 11 ◽

Author(s):

Farshad Moradi Kashkooli ◽

M. Soltani ◽

Mohammad Masoud Momeni ◽

Arman Rahmim

Keyword(s):

Drug Delivery ◽

Treatment Outcomes ◽

Solid Tumors ◽

Tumor Cells ◽

Release Rate ◽

Drug Binding ◽

Binding Affinities ◽

Conventional Chemotherapy ◽

Release Rates ◽

Two Stage

ObjectiveNano-sized drug delivery systems (NSDDSs) offer a promising therapeutic technology with sufficient biocompatibility, stability, and drug-loading rates towards efficient drug delivery to solid tumors. We aim to apply a multi-scale computational model for evaluating drug delivery to predict treatment efficacy.MethodologyThree strategies for drug delivery, namely conventional chemotherapy (one-stage), as well as chemotherapy through two- and three-stage NSDDSs, were simulated and compared. A geometric model of the tumor and the capillary network was obtained by processing a real image. Subsequently, equations related to intravascular and interstitial flows as well as drug transport in tissue were solved by considering real conditions as well as details such as drug binding to cells and cellular uptake. Finally, the role of periodic treatments was investigated considering tumor recurrence between treatments. The impact of different parameters, nanoparticle (NP) size, binding affinity of drug, and the kinetics of release rate, were additionally investigated to determine their therapeutic efficacy.ResultsUsing NPs considerably increases the fraction of killed cells (FKCs) inside the tumor compared to conventional chemotherapy. Tumoral FKCs for two-stage DDS with smaller NP size (20nm) is higher than that of larger NPs (100nm), in all investigate release rates. Slower and continuous release of the chemotherapeutic agents from NPs have better treatment outcomes in comparison with faster release rate. In three-stage DDS, for intermediate and higher binding affinities, it is desirable for the secondary particle to be released at a faster rate, and the drug with slower rate. In lower binding affinities, high release rates have better performance. Results also demonstrate that after 5 treatments with three-stage DDS, 99.6% of tumor cells (TCs) are killed, while two-stage DDS and conventional chemotherapy kill 95.6% and 88.5% of tumor cells in the same period, respectively.ConclusionThe presented framework has the potential to enable decision making for new drugs via computational modeling of treatment responses and has the potential to aid oncologists with personalized treatment plans towards more optimal treatment outcomes.

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Target SARS-CoV-2: Computation of Binding energies with drugs of Dexamethasone/Umifenovir by Molecular Dynamics using OPLS-AA force field

10.21203/rs.3.rs-40785/v1 ◽

2020 ◽

Author(s):

Sk. Md Na ◽

M. Srinivasa R

Keyword(s):

Molecular Dynamics ◽

Force Field ◽

Binding Energies ◽

Drug Binding ◽

Dynamics Simulation ◽

Strong Interactions ◽

Radius Of Gyration ◽

Binding Affinities ◽

Mean Square ◽

Main Protease

Abstract Molecular Dynamics simulation using Gromacs with OPLS-AA force field is performed for 100ns between SARS-CoV-2 main protease and Dexamethasone / Umifenovir drugs at 300 K/1 atm pressure. The trajectory of Root Mean Square Deviation (RMSD) and Radius of Gyration(Rg) emphasized the achievement of equilibrium and compactness. The drug-binding affinities on SARS-CoV-2 main protease are estimated via MM/PBSA method. The sign with magnitude of computed Gibbs free energy indicated the presence of strong interactions between SARS-CoV-2 and drugs of Dexamethasone / Umifenovir. The study revealed that the drug Dexamethasone is more effective over Umifenovir in binding SARS-CoV-2 main protease.

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